Productivity growth and quality changes of hospitals in Taiwan: does ownership matter?

Bakar AF. Approach in inputs & outputs selection of Data Envelopment Analysis (DEA) efficiency measurement in hospitals: A systematic review

The efficiency and productivity evaluation process commonly employs Data Envelopment Analysis (DEA) as a performance tool in numerous fields, such as the healthcare industry (hospitals). Therefore, this review examined various hospital-based DEA articles involving input and output variable selection approaches and the recent DEA developments. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was utilised to extract 89 English articles containing empirical data between 2014 and 2022 from various databases (Web of Science, Scopus, PubMed, ScienceDirect, Springer Link, and Google Scholar). Furthermore, the DEA model parameters were determined using information from previous studies, while the approaches were identified narratively. This review grouped the approaches into four sections: literature review, data availability, systematic method, and expert judgement. An independent single strategy or a combination with other methods was then applied to these approaches. Consequently, the focus of this review on various methodologies employed in hospitals could limit its findings. Alternative approaches or techniques could be utilised to determine the input and output variables for a DEA analysis in a distinct area or based on different perspectives. The DEA application trend was also significantly similar to that of previous studies. Meanwhile, insufficient data was observed to support the usability of any DEA model in terms of fitting all model parameters. Therefore, several recommendations and methodological principles for DEA were proposed after analysing the existing literature.

Cancer stages and mortality risk of breast cancer between women with and without disabilities: A national population-based cohort study in Taiwan

BACKGROUND

The second-most common cause of cancer-related death for women worldwide is breast cancer. However, there is little information about breast cancer among women with disabilities in Taiwan.

OBJECTIVES

This study investigated differences between women with and without disabilities regarding breast cancer stages and evaluated the probability of developing an advanced stage and the mortality risk of breast cancer.

METHODS

This study conducted a nationwide retrospective cohort study using the National Health Insurance Research Database and other nationwide databases. Our participants were newly diagnosed breast cancer patients, including women with and without disabilities, between 2004 and 2010. We matched both of them with propensity score matching methods (1:5), and all were followed up until the end of 2016.

RESULTS

This study included 50,683 participants with breast cancer. After matching, women with disabilities who did not receive breast cancer screening had a more significant proportion of advanced-stage breast cancer (19.95 %) than those without disabilities who did not receive breast cancer screening (16.87 %). After adjusting for related variables, women with disabilities were 1.27 times more likely to have advanced-stage breast cancer than those without disabilities. Additionally, after suffering from breast cancer, individuals with disabilities had a 1.23 times greater mortality risk compared to those without disabilities.

CONCLUSIONS

Although cancer stages were controlled, women with disabilities still had a higher mortality risk of breast cancer. Hence, policymakers should pay more attention to women with disabilities to treat them at an early stage, which can reduce the mortality risk attributable to advanced stages.

Evaluating the comparative efficiency of medical centers in Taiwan: a dynamic data envelopment analysis application

Background

People in Taiwan enjoy comprehensive National Health Insurance coverage. However, under the global budget constraint, hospitals encounter enormous challenges. This study was designed to examine Taiwan medical centers' efficiency and factors that influence it.

Methods

We obtained data from open sources of government routine publications and hospitals disclosed by law to the National Health Insurance Administration, Ministry of Health and Welfare, Taiwan. The dynamic data envelopment analysis (DDEA) model was adopted to estimate all medical centers' efficiencies during 2015–2018. Beta regression models were used to model the efficiency level obtained from the DDEA model. We applied an input-oriented approach under both the constant returns-to-scale (CRS) and variable returns-to-scale (VRS) assumptions to estimate efficiency.

Results

The findings indicated that 68.4% (13 of 19) of medical centers were inefficient according to scale efficiency. The mean efficiency scores of all medical centers during 2015–2018 under the CRS, VRS, and Scale were 0.85, 0.930, and 0.95,respectively. Regression results showed that an increase in the population less than 14 years of age, assets, nurse-patient ratio and bed occupancy rate could increase medical centers' efficiency. The rate of emergency return within 3-day and patient self-pay revenues were associated significantly with reduced hospital efficiency (p < 0.05). The result also showed that the foundation owns medical center has the highest efficiency than other ownership hospitals.

Conclusions

The study results provide information for hospital managers to consider ways they could adjust available resources to achieve high efficiency.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-07869-8.

The Efficiency Evaluation of Municipal-Level Traditional Chinese Medicine Hospitals Using Data Envelopment Analysis After the Implementation of Hierarchical Medical Treatment Policy in Gansu Province, China

Background: Efficiency evaluation is an integral part of new medical reform and is necessary to solve the problem of limited and unbalanced medical resources. This study evaluated the efficiency of municipal-level Traditional Chinese Medicine hospitals by Data Envelopment Analysis application after a hierarchical medical treatment policy was implemented. We propose solutions to the problems existing in hospital operations and promote the utilization efficiency of medical resources in those hospitals. Methods: The sample included all municipal-level TCM hospitals in Gansu province from 2017 to 2019. The DEA-BCC model was employed to evaluate the relative efficiency of hospital operations, and the Manny-Whitney test was used to compare the input and output variables of technical efficiency efficient and inefficient hospitals. Results: From 2017 to 2019, the growth in the number of staff in secondary hospitals (25.88%) was lower than that in tertiary hospitals (31.98%). However, the increase in the number of beds (16.52%) in secondary hospitals was higher than that in tertiary hospitals (−0.30%). 5 (38.46%) achieved DEA efficient in secondary hospitals and 2 (40.00%) in tertiary hospitals. The means of technical efficiency, pure technical efficiency, and scale efficiency in secondary hospitals were 0.812, 0.887, and 0.908, respectively. The means in tertiary hospitals were 0.868, 0.926, and 0.935, respectively. The hospital areas were statistically different between the TE efficient and inefficient hospitals (P<0.05) in secondary hospitals. However, the number of outpatients between the two groups was statistically different (P<0.05) in tertiary hospitals. Conclusion: In this study, the medical and health services of municipal TCM hospitals in Gansu Province have made great progress. Due to the backward economy of Gansu Province, the classification of diagnosis and treatment of diseases was still based on Western medicine, resulting in the slow medical development of some municipal TCM hospitals. TCM hospitals should improve management efficiency, optimize hospital operation scale, improve the utilization efficiency of medical resources and promote efficient hospital development.

Evaluating the Operational Efficiency and Quality of Tertiary Hospitals in Taiwan: The Application of the EBITDA Indicator to the DEA Method and TOBIT Regression

This study estimates the efficiency of 19 tertiary hospitals in Taiwan using a two-stage analysis of Data Envelopment Analysis (DEA) and TOBIT regression. It is a retrospective panel-data study and includes all the tertiary hospitals in Taiwan. The data were sourced from open information hospitals legally required to disclose to the National Health Insurance (NHI) Administration, Ministry of Health and Welfare. The variables, including five inputs (total hospital beds, total physicians, gross equipment, fixed assets net value, the rate of emergency transfer in-patient stay over 48 h) and six outputs (surplus or deficit of appropriation, length of stay, the total relative value units [RVUs] for outpatient services, total RVUs for inpatient services, self-pay income, modified EBITDA) were adopted into the Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) model. In the CCR model, the technical efficiency (TE) from 2015–2018 increases annually, and the average efficiency of all tertiary hospitals is 96.0%. In the BCC model, the highest pure technical efficiency (PTE) was in 2018 and the average efficiency of all medical centers is 99.1%. The average scale efficiency of all medical centers was 96.8% in the BBC model, meaning investment can be reduced by 3.2% and the current production level can be maintained with a fixed return to scale. Correlation coefficient analysis shows that all variables are correlated positively; the highest was the number of beds and the number of days in hospital (r = 0.988). The results show that TE in the CCR model was similar to PTE in the BCC model in four years. The difference analysis shows that more hospitals must improve regarding surplus or deficit of appropriation, modified EBITDA, and self-pay income. TOBIT regression reveals that the higher the bed-occupancy rate and turnover rate of fixed assets, the higher the TE; and the higher number of hospital beds per 100,000 people and turnover rate of fixed assets, the higher the PTE. DEA and TOBIT regression are used to analyze the other factors that affect medical center efficiency, and different categories of hospitals are chosen to assess whether different years or different types of medical centers affect operational performance. This study provides reference values for the improvable directions of relevant large hospitals’ inefficiency decision-making units through reference group analysis and slack variable analysis.

Efficiency and Productivity Change of Public Hospitals in Panama: Do Management Schemes Matter?

In Latin American and Caribbean countries, the main concern of public health care managers has been traditionally placed on problems related to funding, payment mechanisms, and equity of access. However, more recently, there is a growing interest in improving the levels of efficiency and reducing costs in the provision of health services. In this paper we focus on measuring the technical efficiency and productivity change of public hospitals in Panama using bootstrapped Malmquist indices, which allows us to assess the statistical significance of changes in productivity, efficiency, and technology. Specifically, we are interested in comparing the performance of hospitals belonging to the two different management schemes coexisting in the country, the Social Security Fund (SSF) and the Ministry of Health (MoH). Our dataset includes data about 22 public hospitals (11 for each model) during the period between 2005 and 2015. The results showed that the productivity growth of hospitals belonging to the SSF has been much higher than that of the hospitals belonging to the Ministry of Health over the evaluated period (almost 4% compared to 1.5%, respectively). The main explanation for these divergences is the superior growth of technological change in the former hospitals, especially in the final years of the evaluated period.

Multifaced Evidence of Hospital Performance in Pennsylvania

As health care costs and demands for health care services have been rising for decades in the United States, health care reforms have focused on increasing the performance of health care delivery. Competition has been considered as a mechanism to improve the quality of health care services and operational performance. Evidence on health care performance and market competition, however, has not sufficiently been reported to track its progress. The purpose of this study is twofold: First, we measure hospital performance over nine years, using the Malmquist Productivity Index. Second, we examine the impact of market competition on hospital efficiency in Pennsylvania, using a two-stage estimation procedure. The bootstrapped Malmquist productivity indices resulted in noticeable performance improvements. However, no steady performance trends were found during the course of nine years. In examining the impact of market competition, the bootstrapped panel Tobit analysis was applied after computing the efficiency scores with Data Envelopment Analysis. The results of the Tobit model found that hospitals run more efficiently in less competitive regions than in more competitive regions. The finding implies that hospitals underperforming in productivity growth should benchmark best practices of efficient hospitals to improve their productivity level. Another implication is that market competition would not be the best approach to effect the improvement of hospital efficiency in delivering health care services.

Assessing the efficiency of Malaysia health system in COVID-19 prevention and treatment response

Malaysia was faced with a life-threatening crisis in combating COVID-19 with a number of positive cases reaching 5305 and 88 deaths by 18th April 2020 (the first detected case was on 25th January 2020). The government rapidly initiated a public health response and provided adequate medical care to manage the public health crisis during the implementation of movement restrictions, starting 18th March 2020, throughout the country. The objective of this study was to investigate the relative efficiency level of managing COVID-19 in Malaysia using network data envelopment analysis. Malaysia state-level data were extracted from secondary data sources which include variables such as total number of confirmed cases, death cases and recovered cases. These variables were used as inputs and outputs in a network process that consists of 3 sub processes i) community surveillance, ii) medical care I and iii) medical care II. A state-level analysis was performed according to low, medium and high population density categories. The efficiency level of community surveillance was highest compared to medical care processes, indicating that the overall inefficiency is greatly influenced by the inefficiency of the medical care processes rather than the community surveillance process. Results showed that high-density category performed well in both community surveillance and medical care II processes. Meanwhile, low-density category performed better in medical care I process. There was a good overall performance of the health system in Malaysia reflecting a strong preparedness and response level to this pandemic. Furthermore, resource allocation for rapid response was distributed effectively during this challenging period.

Road map for progress and attractiveness of Iranian hospitals by integrating self-organizing map and context-dependent DEA

Hospitals play an important role in healthcare systems and usually stay on the end node of the healthcare chain. Thus, determining their road map to get close to the desired efficiency frontier and developing short-term and long-term plans could help to manage costs and resources, efficiently. As the efficiency frontier depends on the size of the hospital and the complexity of its structure, the homogeneity in benchmarking must be considered. For tackling this problem, the self-organizing map (SOM) is used to create homogeneous groups. On the other hand, data envelopment analysis (DEA) is a well-known methodology for evaluating decision-making units. Each unit obtains the efficiency score based on the ratio of weighted outputs to weighted inputs, where each unit can take the desirable weights for inputs and outputs to provide the maximum value. One of the problems of DEA is the selection of the reference set and distinguishing between the efficient hospitals. To overcome these problems, the context-depended DEA has been applied and the progress and attractiveness of hospitals are obtained. To evaluate the capability of the proposed approach, data of 288 Iranian hospitals are utilized. By applying SOM the hospitals are clustered into appropriate homogeneous groups and by applying context-dependent DEA, the road map for progress and attractiveness of each hospital is determined. In other words, using the proposed approach the hospitals are able to determine the short and long-term goals according to their strategic plans.